Real Estate Data Set

This task involves trying to give an educated guess on a linear regression model for pricing real estate using a real facts date set using numbers and facts. Normally two approaches are used for valuing a real estate property: income and sales comparison. The sales assessment approach values a real estate property based on sale prices of similar properties. In this case the properties with familiar individuality are basically on the same price level, it would be typical to use a linear regression model to complete this demonstration.
Our team will examine the significance of numerous independent variables to a real estate value. Two important strategies to regression testing is to run all test, and run a subset of the test based on a prioritization system. This way one gets the best response to help decide if new or modified code caused errors within the submission.
The data set was selected from the textbook, “ Statistical Techniques in Business & Economics” by Lind, Marchal and Mason. This textbook includes106 sales prices and some key property characteristics. The dependent variable is house price measured in thousands.
The Dependent Variables
1. Selling Price (In Thousands)
2. Number of bedrooms
3. Size of the home in square feet
4. Pool
5. Distance from the center of the city in miles
6. Township
7. Garage attached
8. Number of bathrooms

The graphic figures of stable variables are listed in the table. The team observes cost and dimension have larger scales, which leads us to believe the logarithm of two variables is weak. The team can detect weak linear association among some pairs. Though, this start examination is not adequate enough to sense the significance of descriptive variables to the house cost. When using OLS to estimate a linear regression model with all eight descriptive variables. Regression analysis is used for prediction purposes. A regression model is a statistical model that is used to predict the values of a dependent or